A Toolkit for Ecosystem Ecologists in the Time of Big Science

Ecosystem ecologists are being challenged to address the increasingly complex problems that comprise Big Science. These problems include multiple levels of biological organization that cross multiple interacting temporal and spatial scales, from individual plants, animals, and microbes to landscapes, continents, and the globe. As technology improves, the availability of data, derived data products, and information to address these complex problems are increasing at finer and coarser scales of resolution, and legacy, dark data are brought to light. Data analytics are improving as big data increase in importance in other fields that are improving access to these data. New data sources (crowdsourcing, social media) and ease of communication and collaboration among ecosystem ecologists and other disciplines are increasingly possible via the internet. It is increasingly important that ecosystem ecologists be able to communicate their findings, and to translate their concepts and findings into concrete bits of information that a general public can understand. Traditional approaches that portray ecosystem sciences as a dichotomy between empirical research and theoretical research will keep the field from fully contributing to the complexity of global change questions, and will keep ecosystem ecologists from taking full advantage of the data and technology available. Building on previous research, we describe a more forward-looking, integrated empirical–theoretical modeling approach that is iterative with learning to take advantage of the elements of Big Science. We suggest that training ecosystem ecologists in this integrated approach will be critical to addressing complex Earth system science questions, now and in the future.     

Evolution of natural history information in the 21st century – developing an integrated framework for biological and geographical data

Threats to marine and estuarine species operate over many spatial scales, from nutrient enrichment at the watershed/estuarine scale to invasive species and climate change at regional and global scales. To help address research questions across these scales, we provide here a standardized framework for a biogeographical information system containing queriable biological data that allows extraction of information on multiple species, across a variety of spatial scales based on species distributions, natural history attributes and habitat requirements. As scientists shift from research on localized impacts on individual species to regional and global scale threats, macroecological approaches of studying multiple species over broad geographical areas are becoming increasingly important. The standardized framework described here for capturing and integrating biological and geographical data is a critical first step towards addressing these macroecological questions and we urge organizations capturing biogeoinformatics data to consider adopting this framework.     

Ecosystem oceanography for global change in fisheries

Overexploitation and climate change are increasingly causing unanticipated changes in marine ecosystems, such as higher variability in fish recruitment and shifts in species dominance. An ecosystem-based approach to fisheries attempts to address these effects by integrating populations, food webs and fish habitats at different scales. Ecosystem models represent indispensable tools to achieve this objective. However, a balanced research strategy is needed to avoid overly complex models. Ecosystem oceanography represents such a balanced strategy that relates ecosystem components and their interactions to climate change and exploitation. It aims at developing realistic and robust models at different levels of organisation and addressing specific questions in a global change context while systematically exploring the ever-increasing amount of biological and environmental data.     

A method for quantifying consistency in animal distributions using survey data

The degree of consistency with which groups of animals use the landscape is determined by a variety of ecological processes that influence their movements and patterns of habitat use. We developed a technique termed Distributional Consistency that uses survey data of unmarked individuals to quantify temporal consistency in their spatial distribution, while accounting for changes in population size. Distributional consistency is quantified by comparing the observed distribution patterns to all theoretically possible distribution patterns of observed individuals, leading to a proportional score between 0 and 1, reflecting increasingly consistent use of sites within a region. The technique can be applied to survey data for any taxa across a range of spatial and temporal scales. We suggest ways in which distributional consistency could provide inferences about the dispersal and habitat decisions of individuals, and the scales at which these decisions operate. Distributional consistency integrates spatial and temporal processes to quantify an important characteristic of different habitats and their use by populations, which in turn will be particularly useful in complimenting and interpreting other ecological measures such as population density and stability. The technique can be applied to many existing data sets to investigate and evaluate a range of important ecological questions using simple survey data.     

Vegetation demographics in Earth System Models: A review of progress and priorities

Numerous current efforts seek to improve the representation of ecosystem ecology and vegetation demographic processes within Earth System Models (ESMs). These developments are widely viewed as an important step in developing greater realism in predictions of future ecosystem states and fluxes. Increased realism, however, leads to increased model complexity, with new features raising a suite of ecological questions that require empirical constraints. Here, we review the developments that permit the representation of plant demographics in ESMs, and identify issues raised by these developments that highlight important gaps in ecological understanding. These issues inevitably translate into uncertainty in model projections but also allow models to be applied to new processes and questions concerning the dynamics of real‐world ecosystems. We argue that stronger and more innovative connections to data, across the range of scales considered, are required to address these gaps in understanding. The development of first‐generation land surface models as a unifying framework for ecophysiological understanding stimulated much research into plant physiological traits and gas exchange. Constraining predictions at ecologically relevant spatial and temporal scales will require a similar investment of effort and intensified inter‐disciplinary communication.     

Synaptic protein degradation by the ubiquitin proteasome system

Synaptic plasticity -- the modulation of synaptic strength between a presynaptic terminal and a postsynaptic dendrite -- is thought to be a mechanism that underlies learning and memory. It has become increasingly clear that regulated protein synthesis is an important mechanism used to regulate the protein content of synapses that results in changes in synaptic strength. Recent experiments have highlighted a role for the opposing process, that is, regulated protein degradation via the ubiquitin-proteasome system, in synaptic plasticity. These recent findings raise exciting questions as to how proteasomal activity can regulate synapses over different temporal and spatial scales.     

History matters: ecometrics and integrative climate change biology

Climate change research is increasingly focusing on the dynamics among species, ecosystems and climates. Better data about the historical behaviours of these dynamics are urgently needed. Such data are already available from ecology, archaeology, palaeontology and geology, but their integration into climate change research is hampered by differences in their temporal and geographical scales. One productive way to unite data across scales is the study of functional morphological traits, which can form a common denominator for studying interactions between species and climate across taxa, across ecosystems, across space and through time-an approach we call 'ecometrics'. The sampling methods that have become established in palaeontology to standardize over different scales can be synthesized with tools from community ecology and climate change biology to improve our understanding of the dynamics among species, ecosystems, climates and earth systems over time. Developing these approaches into an integrative climate change biology will help enrich our understanding of the changes our modern world is undergoing.     

Using Antifungal Pharmacodynamics to Improve Patient Outcomes

In vitro and in vivo studies of available and investigational antifungals have broadened our understanding of the pharmacodynamics of these agents as well as the pharmacokinetic/pharmacodynamic characteristics that are associated with efficacy. These data are increasingly being used as surrogate means to answer questions about dosing and administration of antimicrobial agents in order to improve outcomes in patients with invasive fungal infections, as these questions are difficult to answer in clinical trials. The objective of this article is to review the pharmacodynamic activity of widely used classes of antifungal agents, including the azoles, amphotericin B, and the echinocandins, discuss the pharmacokinetic/pharmacodynamic parameters associated with efficacy of these agents in preclinical studies, and describe how this information is being translated into the clinical arena to optimize patient outcomes.     

The difficult legacy of Turing’s wager

Neuron modeling may be said to have originated with the Hodgkin and Huxley action potential model in 1952 and Rall’s models of integrative activity of dendrites in 1964. Over the ensuing decades, these approaches have led to a massive development of increasingly accurate and complex data-based models of neurons and neuronal circuits. ModelDB was founded in 1996 to support this new field and enhance the scientific credibility and utility of computational neuroscience models by providing a convenient venue for sharing them. It has grown to include over 1100 published models covering more than 130 research topics. It is actively curated and developed to help researchers discover and understand models of interest. ModelDB also provides mechanisms to assist running models both locally and remotely, and has a graphical tool that enables users to explore the anatomical and biophysical properties that are represented in a model. Each of its capabilities is undergoing continued refinement and improvement in response to user experience. Large research groups (Allen Brain Institute, EU Human Brain Project, etc.) are emerging that collect data across multiple scales and integrate that data into many complex models, presenting new challenges of scale. We end by predicting a future for neuroscience increasingly fueled by new technology and high performance computation, and increasingly in need of comprehensive user-friendly databases such as ModelDB to provide the means to integrate the data for deeper insights into brain function in health and disease.     

Nutritional Systems Biology Modeling: From Molecular Mechanisms to Physiology

The use of computational modeling and simulation has increased in many biological fields, but despite their potential these techniques are only marginally applied in nutritional sciences. Nevertheless, recent applications of modeling have been instrumental in answering important nutritional questions from the cellular up to the physiological levels. Capturing the complexity of today''s important nutritional research questions poses a challenge for modeling to become truly integrative in the consideration and interpretation of experimental data at widely differing scales of space and time. In this review, we discuss a selection of available modeling approaches and applications relevant for nutrition. We then put these models into perspective by categorizing them according to their space and time domain. Through this categorization process, we identified a dearth of models that consider processes occurring between the microscopic and macroscopic scale. We propose a “middle-out” strategy to develop the required full-scale, multilevel computational models. Exhaustive and accurate phenotyping, the use of the virtual patient concept, and the development of biomarkers from “-omics” signatures are identified as key elements of a successful systems biology modeling approach in nutrition research—one that integrates physiological mechanisms and data at multiple space and time scales.     

The EcoData Retriever: Improving Access to Existing Ecological Data

Ecological research relies increasingly on the use of previously collected data. Use of existing datasets allows questions to be addressed more quickly, more generally, and at larger scales than would otherwise be possible. As a result of large-scale data collection efforts, and an increasing emphasis on data publication by journals and funding agencies, a large and ever-increasing amount of ecological data is now publicly available via the internet. Most ecological datasets do not adhere to any agreed-upon standards in format, data structure or method of access. Some may be broken up across multiple files, stored in compressed archives, and violate basic principles of data structure. As a result acquiring and utilizing available datasets can be a time consuming and error prone process. The EcoData Retriever is an extensible software framework which automates the tasks of discovering, downloading, and reformatting ecological data files for storage in a local data file or relational database. The automation of these tasks saves significant time for researchers and substantially reduces the likelihood of errors resulting from manual data manipulation and unfamiliarity with the complexities of individual datasets.     

The Association of Biomolecular Resource Facilities Proteomics Research Group 2006 study: relative protein quantitation

The determination of differences in relative protein abundance is a critical aspect of proteomics research that is increasingly used to answer diverse biological questions. The Association of Biomolecular Resource Facilities Proteomics Research Group 2006 study was a quantitative proteomics project in which the aim was to determine the identity and the relative amounts of eight proteins in two mixtures. There are numerous methodologies available to study the relative abundance of proteins between samples, but to date, there are few examples of studies that have compared these different approaches. For the 2006 Proteomics Research Group study, there were 52 participants who used a wide variety of gel electrophoresis-, HPLC-, and mass spectrometry-based methods for relative quantitation. The quantitative data arising from this study were evaluated along with several other experimental details relevant to the methodologies used.     

Phylogenetic analysis of community assembly and structure over space and time

Evolutionary ecologists are increasingly combining phylogenetic data with distributional and ecological data to assess how and why communities of species differ from random expectations for evolutionary and ecological relatedness. Of particular interest have been the roles of environmental filtering and competitive interactions, or alternatively neutral effects, in dictating community composition. Our goal is to place current research within a dynamic framework, specifically using recent phylogenetic studies from insular environments to provide an explicit spatial and temporal context. We compare communities over a range of evolutionary, ecological and geographic scales that differ in the extent to which speciation and adaptation contribute to community assembly and structure. This perspective allows insights into the processes that can generate community structure, as well as the evolutionary dynamics of community assembly.     

If neuroimaging is the answer,what is the question?

It is unclear that we will come to a better understanding of mental processes simply by observing which neural loci are activated while subjects perform a task. Rather, I suggest here that it is better to come armed with a question that directs one to design tasks in ways that take advantage of the strengths of neuroimaging techniques (particularly positron emission tomography and functional magnetic resonance imaging). Here I develop a taxonomy of types of questions that can be easily addressed by such techniques. The first class of questions focuses on how information processing is implemented in the brain; these questions can be posed at a very coarse scale, focusing on the entire system that confers a particular ability, or at increasingly more specific scales, ultimately focusing on individual structures or processes. The second class of questions focuses on specifying when particular processes and structures are invoked; these questions focus on how one can use patterns of activation to infer that specific processes and structures were invoked, and on how processing changes in different circumstances. The use of neuroimaging to address these questions is illustrated with results from experiments on visual cognition, and caveats regarding the logic of inference in each case are noted. Finally, the necessary interplay between neuroimaging and behavioural studies is stressed.     

Miniaturized analytical assays in biotechnology

Biotechnology today is a well-established paradigm in many areas of human endeavor, such as the pharmaceutical industry, agriculture, management of the environment and many others. Meanwhile, biology is undergoing a spectacular transition: whereas systematic biology was replaced gradually by molecular biology, the latter is rapidly being transformed into a new systematic era in which entire genomes are being charted by ever more sophisticated analytical techniques.In the wake of this onslaught of data, new fields are germinating, such as bioinformatics in an attempt to find answers to fundamental questions, answers that may be hidden in the massive amounts of data already available today.     

COMPARISON OF ATTRIBUTE LIKING AND JAR SCALES TO EVALUATE THE ADEQUACY OF SENSORY ATTRIBUTES OF MILK DESSERTS

Just-about-right (JAR) scales and attribute liking questions are usually used to study consumer perception of the sensory characteristics of food products. The aim of the present work was to compare the performance of attribute liking and JAR scales to evaluate consumers' perceived adequacy of flavor and texture of milk puddings. Two groups of consumers were asked to evaluate eight milk desserts using (1) overall liking followed by attribute liking for texture and flavor and (2) overall liking followed by JAR scales for thickness, creaminess, sweetness and vanilla flavor. Overall liking scores were significantly different when JAR scales or attribute liking questions were considered. Texture, flavor and overall liking scores were highly correlated to each other, providing the same information. JAR scales correlated better with the intensity of sensory attributes evaluated by a trained sensory panel, being JAR percentages a reliable tool to study the adequacy of sensory attributes.

PRACTICAL APPLICATIONS

Results from the present work showed that consumers might not be able to independently evaluate their liking of different sensory attributes of a product. For this reason, the use of attribute liking questions for studying the adequacy of sensory attributes in complex products would not be recommended. JAR scales were better indicators of the adequacy of sensory attributes. Consumers were able to independently evaluate texture and flavor attributes using JAR scales. However, the influence of JAR scales on overall liking scores should be taken into account when including these scales on consumer studies.     

Imaging spectroscopy links aspen genotype with below-ground processes at landscape scales

Fine-scale biodiversity is increasingly recognized as important to ecosystem-level processes. Remote sensing technologies have great potential to estimate both biodiversity and ecosystem function over large spatial scales. Here, we demonstrate the capacity of imaging spectroscopy to discriminate among genotypes of Populus tremuloides (trembling aspen), one of the most genetically diverse and widespread forest species in North America. We combine imaging spectroscopy (AVIRIS) data with genetic, phytochemical, microbial and biogeochemical data to determine how intraspecific plant genetic variation influences below-ground processes at landscape scales. We demonstrate that both canopy chemistry and below-ground processes vary over large spatial scales (continental) according to aspen genotype. Imaging spectrometer data distinguish aspen genotypes through variation in canopy spectral signature. In addition, foliar spectral variation correlates well with variation in canopy chemistry, especially condensed tannins. Variation in aspen canopy chemistry, in turn, is correlated with variation in below-ground processes. Variation in spectra also correlates well with variation in soil traits. These findings indicate that forest tree species can create spatial mosaics of ecosystem functioning across large spatial scales and that these patterns can be quantified via remote sensing techniques. Moreover, they demonstrate the utility of using optical properties as proxies for fine-scale measurements of biodiversity over large spatial scales.     

Scale-space analysis of time series in circulatory research

Statistical analysis of time series is still inadequate within circulation research. With the advent of increasing computational power and real-time recordings from hemodynamic studies, one is increasingly dealing with vast amounts of data in time series. This paper aims to illustrate how statistical analysis using the significant nonstationarities (SiNoS) method may complement traditional repeated-measures ANOVA and linear mixed models. We applied these methods on a dataset of local hepatic and systemic circulatory changes induced by aortoportal shunting and graded liver resection. We found SiNoS analysis more comprehensive when compared with traditional statistical analysis in the following four ways: 1) the method allows better signal-to-noise detection; 2) including all data points from real time recordings in a statistical analysis permits better detection of significant features in the data; 3) analysis with multiple scales of resolution facilitates a more differentiated observation of the material; and 4) the method affords excellent visual presentation by combining group differences, time trends, and multiscale statistical analysis allowing the observer to quickly view and evaluate the material. It is our opinion that SiNoS analysis of time series is a very powerful statistical tool that may be used to complement conventional statistical methods.     

EFFECT OF QUESTION ORDER ON SENSORY PERCEPTION AND PREFERENCE IN CENTRAL LOCATION TRIALS

In the running of consumer studies aimed at obtaining information about products and services, it has become increasingly popular to include some sensory type questions about the characteristics of the test product as well as the standard question about overall preference. The implications of this for the resultant data are complex, and as Sudman and Schwarz (1989) point out question order effects have been implicated as one of the major causes of unreplicated or unexpected findings in survey research. This study aims to establish the effect of question order on overall preference scores and sensory scores in consumer studies. Results indicate that there is a significant effect of asking sensory questions on overall preference ratings and the position of the preference questions (relative to the sensory questions) also has implications for overall ratings of preference.